article thumbnail

The Science of T20 Cricket: Decoding Player Performance with Predictive Modeling

Analytics Vidhya

This article explores how data analytics optimizes strategies by leveraging player performances and opposition weaknesses. Python programming predicts player performances, aiding team selections and game tactics.

article thumbnail

Predictive Models Are Nothing Without Trust

Cloudera

Everyone may answer and say, informed decision making, generate profit, improve customer relations optimization. Ryan: Instead of looking in the past, we’ve built a predictive model and its origins come from people trusting in usthey ask us about different scenarios. Why am I doing this? Why are we doing this?

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Rapidminer Platform Supports Entire Data Science Lifecycle

David Menninger's Analyst Perspectives

Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. It can support AI/ML processes with data preparation, model validation, results visualization and model optimization.

article thumbnail

Optimize your workloads with Amazon Redshift Serverless AI-driven scaling and optimization

AWS Big Data

To address this requirement, Redshift Serverless launched the artificial intelligence (AI)-driven scaling and optimization feature, which scales the compute not only based on the queuing, but also factoring data volume and query complexity. The slider offers the following options: Optimized for cost – Prioritizes cost savings.

article thumbnail

The key to operational AI: Modern data architecture

CIO Business Intelligence

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

article thumbnail

How to Set AI Goals

O'Reilly on Data

Likewise, AI doesn’t inherently optimize supply chains, detect diseases, drive cars, augment human intelligence, or tailor promotions to different market segments. Therefore, AI techniques don’t just solve real-world problems out of the box. They don’t automatically generate revenue and growth, maximize ROI, or keep users engaged and loyal.

article thumbnail

Beyond the hype: Do you really need an LLM for your data?

CIO Business Intelligence

In retail, they can personalize recommendations and optimize marketing campaigns. Even basic predictive modeling can be done with lightweight machine learning in Python or R. Training and running these models require massive computing power, leading to a significant carbon footprint. Ive seen this firsthand.